عنوان

Clinically-Guided Engineering for Scaling Wearable Health Monitoring:

TL0zh988z1

انگلیسی

Clinically-Guided Engineering for Scaling Wearable Health Monitoring:

[Thesis]

Kang, Dae

Stretchable Adhesive Sensors and Dynamic Statistical Analyses in Sleep Medicine

Coleman, Todd P

2017

Coleman, Todd P

2017

Background & Objective: Although the reasons behind the biological necessity of sleep remain to be fully discovered, emerging data show that sleep is crucial for maintaining physical health and mental well-being. Despite its great importance, nearly 40% of US adults experience problems with sleep, ranging from sleep deprivation (decreased total sleep time) to sleep disorders (e.g. obstructive sleep apnea, OSA). Furthermore, many millions of these individuals with sleep disorders are severely underdiagnosed. The major limitation to improved understanding of sleep in health, and streamlined identification of at-risk individuals, is the inability to objectively measure sleep at scale. Gold standard sleep measurement by polysomnography (PSG) requires bulky, obtrusive measurements of multiple biological signals that interfere with sleep. Analysis of sleep measurements undergo a manual/visual process that is lengthy, expensive, and subjective. These impediments to sleep make testing beyond a single night in selected patients impractical, and make addressing growing public health issues in sleep seemingly impossible. Methods & Results: To address these shortcomings in the current sleep medicine paradigm, a systems approach to clinically-guided engineering spanning sensing and analytical aspects of sleep monitoring is described herein. Adhesive-integrated stretchable sensors were engineered to achieve scalable, peel-and-stick clinical sensing on ubiquitously used clinical adhesives, resulting in a cost-effective, facile, and familiar means for clinical implementation of unobtrusive electrophysiological sensing technology. Simultaneously and in collaboration with sleep medicine clinicians, small-data statistical analytics were engineered to dynamically assess clinical-grade sleep architecture from single-channel electrophysiology, the results of which are on par with in-lab PSG for both healthy/normal individuals and patients with OSA. Conclusion & Significance: The result of this applied clinical engineering research is a suite of tools that emphasizes miniaturization of sleep sensing methods, complemented by analytical endeavors to generate clinical-grade sleep measures from consequential small-data platforms. The gestalt is not only envisioned for objective assessment of sleep-based clinical outcomes, but to also stimulate basic science investigation of sleep dynamics and their context in other comorbidities. By performing engineering in medicine in a systemic manner, the principles of this work can be extrapolated for the continued advancement of general wearable health monitoring practice.

Pigeon, Jeremy James

Kang, Dae

UC San Diego

p

[Thesis]

276903

a

Y